The impact of signal pre-processing on the final interpretation of analytical outcomes – A tutorial

Determination of time since deposition of bloodstains through NIR and UV-Vis spectroscopy - A critical comparison

Time elapsed since bloodstain deposition is a crucial aspect in forensic investigations, where non-destructive spectroscopic methods play a pivotal role. While extensive research has been conducted by UV-Vis spectroscopy, showcasing its utility in specific cases, there is still a paucity of studies based on NIR spectroscopy, which has the potential to overcome the limitations of the UV-Vis-based methods. To compensate for this disequilibrium, the present study aimed to evaluate the NIR applicability for estimating the age of forensic bloodstains and develop a performance comparison with UV-Vis spectroscopy methods. Capillary blood was sampled and subjected to a 16-day aging, during which it was repeatedly analyzed using both spectroscopic methods. Subsequently, chemometric analysis was applied to process the spectral data and independently assess the methods' performance. Classical preprocessing transforms (i.e., Savitzky-Golay derivatives and SNV transform) were used together with more targeted strategies, such as class centering, whose benefit was highlighted by PCA. Lastly, PLS regression models were computed to evaluate the effectiveness of both spectroscopic methods in estimating the time elapsed since blood trace deposition. Comparable root mean square errors in prediction (RMSEP) - 40 and 55 h for UV-Vis and NIR spectroscopy, respectively - were observed for both techniques, featuring an improvement with respect to the existing literature for NIR spectroscopy. Data fusion strategies for a multi-instrumental platform were also explored, evaluating advantages and disadvantages of low-level and mid-level approaches. The results indicated that NIR spectroscopy integrated with adequate chemometric strategies deserves increased appreciation in forensic bloodstain dating.

Stacking and ridge regression-based spectral ensemble preprocessing method and its application in near-infrared spectral analysis

Spectral preprocessing techniques can, to a certain extent, eliminate irrelevant information, such as current noise and stray light from spectral data, thereby enhancing the performance of prediction models. However, current preprocessing techniques mostly attempt to find the best single preprocessing method or their combination, overlooking the complementary information among different preprocessing methods. These preprocessing techniques fail to maximize the utilization of useful information in spectral data and restrict the performance of prediction models. This study proposed a spectral ensemble preprocessing method based on the rapidly developing ensemble learning methods in recent years and the ridge regression (RR) model, named stacking preprocessing ridge regression (SPRR), to address the aforementioned issues. Different from conventional ensemble learning methods, the proposed SPRR method applied multiple different preprocessing techniques to the original spectral data, generating multiple preprocessed datasets. These datasets were then individually inputted into RR base models for training. Ultimately, RR still served as the meta-model, integrating the output results of each RR base model through stacking. This approach not only produced diversity in base models but also achieved higher accuracy and lower computational complexity by using a single type of base model. On the apple spectral dataset collected by our team, correlation analysis showed significant complementary information among the data produced by different preprocessing techniques. This provided robust theoretical support for the proposed SPRR method. By introducing the currently popular averaging ensemble preprocessing method in a comparative experiment, the results of applying the proposed SPRR method to six datasets (apple, meat, wheat, olive oil, tablet, and corn) demonstrated that compared to the single preprocessing method and averaging ensemble preprocessing method, SPRR yielded the best accuracy and reliability for all six datasets. Furthermore, under the same conditions of the training and test datasets, the proposed SPRR method demonstrated better performance than the four commonly used ensemble preprocessing methods.

A decision support tool to analyze the properties of wheat, cocoa beans and mangoes from their NIR spectra

Near infrared spectroscopy (NIRS) is an analytical technique that offers a real advantage over laboratory analysis in the food industry due to its low operating costs, rapid analysis, and non-destructive sampling technique. Numerous studies have shown the relevance of NIR spectra analysis for assessing certain food properties with the right calibration. This makes it useful in quality control and in the continuous monitoring of food processing. However, the NIR calibration process is difficult and time-consuming. Analysis methods and techniques vary according to the configuration of the NIR instrument, the sample to be analyzed and the attribute that is to be predicted. This makes calibration a challenge for many manufacturers. This paper aims to provide a data-driven methodology for developing a decision support tool based on the smart selection of NIRS wavelength to assess various food properties. The decision support tool based on the methodology has been evaluated on samples of cocoa beans, grains of wheat and mangoes. Promising results were obtained for each of the selected models for the moisture and fat content of cocoa beans (R2cv: 0.90, R2test: 0.93, RMSEP: 0.354%; R2cv: 0.73, R2test: 0.79, RMSEP: 0.913%), acidity and vitamin C content of mangoes (R2cv: 0.93, R2test: 0.97, RMSEP: 17.40%; R2cv: 0.66, R2test: 0.46, RMSEP: 0.848%), and protein content of wheat-DS2 (R2cv: 0.90, R2test:0.92, RMSEP: 0.490%) respectively. Moreover, the proposed approach allows results to be obtained that are better than benchmarks for the moisture and protein content of wheat-DS1 (R2cv: 0.90, R2test: 94, RMSEP: 0.337%; R2cv: 0.99, R2test: 0.99, RMSEP: 0.177%), respectively. PRACTICAL APPLICATION: This research introduces a practical tool aimed at determining the quality of food by identifying specific light wavelengths. However, it is important to acknowledge potential challenges, such as overfitting. Before implementation, it is crucial for further research to address and mitigate the issues to ensure the reliability and accuracy of the solution. If successfully applied, this tool could significantly enhance the accuracy of near-infrared spectroscopy in assessing food quality attributes. This advancement would provide invaluable support for decision-making in industries involved in food production, ultimately leading to better overall product quality for consumers.

Profiling of seized Cannabis sativa L. flowering tops by means of microwave-assisted hydro distillation and gas chromatography analyses

This research aimed to support police forces in their battle against illicit drug trafficking by means of a multi-technique approach, based on gas chromatography. In detail, this study was focused on the profiling of volatile substances in narcotic Cannabis sativa L. flowering tops. For this purpose, the Scientific Investigation Department, RIS Carabinieri of Messina, provided 25 seized samples of Cannabis sativa L. The content of Δ9-tetrahydrocannabinol (THC), useful to classify cannabis plant as hemp (≤ 0.2 %) or as marijuana (> 0.2 %), was investigated. Essential oils of illicit drug samples were extracted using a microwave-assisted hydro-distillation (MAHD) system; GC-MS and GC-FID analytical techniques were used for the characterization of the terpenes and terpenoids fingerprint. Furthermore, the enantiomeric and carbon isotopic ratios of selected chiral compounds were investigated using a heart-cutting multidimensional GC (MDGC) approach. The latter exploited a combination of an apolar column in the first dimension, and a chiral cyclodextrin-based column in the second one, prior to parallel isotope-ratio mass spectrometry (C-IRMS) and MS detection. Finally, all the data were gathered into a statistical model, to demonstrate the existence of useful parameters to be used for the classification of seized samples.

Analytical Chemistry Strategies in the Use of Miniaturised NIR Instruments: An Overview

Miniaturized NIR instruments have been increasingly used in the last years, and they have become useful tools for many applications on a broad variety of samples. This review focuses on miniaturized NIR instruments from an analytical point of view, to give an overview of the analytical strategies used in order to help the reader to set up their own analytical methods, from the sampling to the data analysis. It highlights the uses of these instruments, providing a critical discussion including current and future trends.

Analysis of Clinical Samples of Pancreatic Cyst's Lesions with A Multi-Analyte Bioelectronic Simot Array Benchmarked Against Ultrasensitive Chemiluminescent Immunoassay

Pancreatic cancer, ranking as the third factor in cancer-related deaths, necessitates enhanced diagnostic measures through early detection. In response, SiMoT-Single-molecule with a large Transistor multiplexing array, achieving a Technology Readiness Level of 5, is proposed for a timely identification of pancreatic cancer precursor cysts and is benchmarked against the commercially available chemiluminescent immunoassay SIMOA (Single molecule array) SP-X System. A cohort of 39 samples, comprising 33 cyst fluids and 6 blood plasma specimens, undergoes detailed examination with both technologies. The SiMoT array targets oncoproteins MUC1 and CD55, and oncogene KRAS, while the SIMOA SP-X planar technology exclusively focuses on MUC1 and CD55. Employing Principal Component Analysis (PCA) for multivariate data processing, the SiMoT array demonstrates effective discrimination of malignant/pre-invasive high-grade or potentially malignant low-grade pancreatic cysts from benign non-mucinous cysts. Conversely, PCA analysis applied to SIMOA assay reveals less effective differentiation ability among the three cyst classes. Notably, SiMoT unique capability of concurrently analyzing protein and genetic markers with the threshold of one single molecule in 0.1 mL positions it as a comprehensive and reliable diagnostic tool. The electronic response generated by the SiMoT array facilitates direct digital data communication, suggesting potential applications in the development of field-deployable liquid biopsy.

Rapid determination of geographical authenticity and pungency intensity of the red Sichuan pepper (Zanthoxylum bungeanum) using differential pulse voltammetry and machine learning algorithms

The development of an analytical method for assessing pungency intensity and determining geographical origins is crucial for evaluating the quality of visually similar Zanthoxylum bungeanum pericarp (PZB). This study analyzed 210 PZB samples from 14 origins across China, focusing on origin adulteration identification and pungency intensity using a combination of differential pulse voltammetry (DPV) and machine learning algorithms. The artificial neural network (ANN) and K-nearest neighbor (KNN) algorithms provided the highest accuracy in origin identification (100 %) and adulteration detection (97.9 %) respectively. Moreover, the ANN excelled in predicting pungency intensity (R2 = 0.918). Assessment via feature importance analysis of DPV features revealed that segments of polyphenols (0.34-0.52 V and 1.0-1.2 V) and alkylamides (1.0-1.2 V) contributed significantly to the PZB pungency intensity. These findings highlight the potential of DPV as a reliable method for assessing the quality of PZB, and offer a promising solution for ensuring the geographical authenticity of this important crop.

Critical Review of Selected Analytical Platforms for GC-MS Metabolomics Profiling-Case Study: HS-SPME/GC-MS Analysis of Blackberry's Aroma

Data processing and data extraction are the first, and most often crucial, steps in metabolomics and multivariate data analysis in general. There are several software solutions for these purposes in GC-MS metabolomics. It becomes unclear which platform offers what kind of data and how that information influences the analysis's conclusions. In this study, selected analytical platforms for GC-MS metabolomics profiling, SpectConnect and XCMS as well as MestReNova software, were used to process the results of the HS-SPME/GC-MS aroma analyses of several blackberry varieties. In addition, a detailed analysis of the identification of the individual components of the blackberry aroma club varieties was performed. In total, 72 components were detected in the XCMS platform, 119 in SpectConnect, and 87 and 167 in MestReNova, with automatic integral and manual correction, respectively, as well as 219 aroma components after manual analysis of GC-MS chromatograms. The obtained datasets were fed, for multivariate data analysis, to SIMCA software, and underwent the creation of PCA, OPLS, and OPLS-DA models. The results of the validation tests and VIP-pred. scores were analyzed in detail.

Prediction of melanin content of Fonsecaea pedrosoi using Fourier transform infrared spectroscopy (FTIR) and chemometrics

Fungal melanin contributes to the survival and virulence of pathogenic fungi, such as Fonsecaea pedrosoi, which is responsible for causing chromoblastomycosis. The objective of this study was to employ Fourier transform infrared spectroscopy (FTIR) to predict the melanin content of F. pedrosoi. The melanin content, in percentage, was previously determined using gravimetry for twenty-six clinical isolates. Quintuplicate spectra of each isolate were obtained using attenuated total reflection (ATR) within the range of 4000 to 650 cm-1. To predict the melanin content, modeling was performed using partial least squares regression (PLS) in the region 1800 - 750 cm-1. Two models were tested: PLS and successive projections algorithms for interval selection in partial least squares (iSPA-PLS). The best modeling results were achieved using iSPA-PLS with one factor. The calibration set exhibited a determination coefficient (R2) of 0.9745 and a root mean square error of cross-validation (RMSECV) of 0.0977. In the prediction set, the R2 value was 0.9711, and the root mean square error of prediction (RMSEP) was 0.0999. Modeling with FTIR and multivariate calibration provides a valuable means of predicting fungal melanin content, which is simpler and more robust, thereby contributing to the advancement of this field of study.

Machine learning on cardiotocography data to classify fetal outcomes: A scoping review

INTRODUCTION

Uterine contractions during labour constrict maternal blood flow and oxygen delivery to the developing baby, causing transient hypoxia. While most babies are physiologically adapted to withstand such intrapartum hypoxia, those exposed to severe hypoxia or with poor physiological reserves may experience neurological injury or death during labour. Cardiotocography (CTG) monitoring was developed to identify babies at risk of hypoxia by detecting changes in fetal heart rate (FHR) patterns. CTG monitoring is in widespread use in intrapartum care for the detection of fetal hypoxia, but the clinical utility is limited by a relatively poor positive predictive value (PPV) of an abnormal CTG and significant inter and intra observer variability in CTG interpretation. Clinical risk and human factors may impact the quality of CTG interpretation. Misclassification of CTG traces may lead to both under-treatment (with the risk of fetal injury or death) or over-treatment (which may include unnecessary operative interventions that put both mother and baby at risk of complications). Machine learning (ML) has been applied to this problem since early 2000 and has shown potential to predict fetal hypoxia more accurately than visual interpretation of CTG alone. To consider how these tools might be translated for clinical practice, we conducted a review of ML techniques already applied to CTG classification and identified research gaps requiring investigation in order to progress towards clinical implementation.

MATERIALS AND METHOD

We used identified keywords to search databases for relevant publications on PubMed, EMBASE and IEEE Xplore. We used Preferred Reporting Items for Systematic Review and Meta-Analysis for Scoping Reviews (PRISMA-ScR). Title, abstract and full text were screened according to the inclusion criteria.

RESULTS

We included 36 studies that used signal processing and ML techniques to classify CTG. Most studies used an open-access CTG database and predominantly used fetal metabolic acidosis as the benchmark for hypoxia with varying pH levels. Various methods were used to process and extract CTG signals and several ML algorithms were used to classify CTG. We identified significant concerns over the practicality of using varying pH levels as the CTG classification benchmark. Furthermore, studies needed to be more generalised as most used the same database with a low number of subjects for an ML study.

CONCLUSION

ML studies demonstrate potential in predicting fetal hypoxia from CTG. However, more diverse datasets, standardisation of hypoxia benchmarks and enhancement of algorithms and features are needed for future clinical implementation.

A Multivariate Analysis-Driven Workflow to Tackle Uncertainties in Miniaturized NIR Data

This study focuses on exploring and understanding measurement errors in analytical procedures involving miniaturized near-infrared instruments. Despite recent spreading in different application fields, there remains a lack of emphasis on the accuracy and reliability of these devices, which is a critical concern for accurate scientific outcomes. The study investigates multivariate measurement errors, revealing their complex nature and the influence that preprocessing techniques can have. The research introduces a possible workflow for practical error analysis in experiments involving diverse samples and instruments. Notably, it investigates how sample characteristics impact errors in the case of solid pills and tablets, typical pharmaceutical samples. ASCA was used for understanding critical instrumental factors and the potential and limitations of the method in the current application were discussed. The joint interpretation of multivariate error matrices and their resume through image histograms and K index are discussed in order to evaluate the impact of common preprocessing methods and to assess their influence on signals.

ATR-FTIR spectroscopy combined with chemometrics to assess the spectral markers of irradiated baijius and their potential application in irradiation dose control

Although some methods have been proposed for the identification of irradiated baijius, they are often costly, time-consuming, and destructive. It is also unclear what instrumentation can be used to fully characterize the quality changes in irradiated baijius. To address this issue, this study pioneers the use of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy in combination with chemometrics to open up new avenues for characterizing irradiated baijius and their quality control. Principal component analysis, five spectral pre-processing methods (Savitzky-Golay smoothing (S-G), second-order derivative (SD), multiple scattering correction (MSC), S-G + SD and S-G + MSC), five wavelength selection methods (random forest variable importance (RFVI), two-dimensional correlation spectroscopy (2D-COS), variable importance in projection (VIP), ReliefF, and Venn), and three classification models (partial least squares-discriminant analysis (PLS-DA), random forest (RF), and grasshopper optimization algorithm-based support vector machine (GOA-SVM)) were integrated into the analytical framework of ATR-FTIR spectroscopy, aiming to accurately identify baijiu samples according to different irradiation doses and to search for irradiation-induced spectral difference characteristics (spectral markers). The results showed that SD was the best spectral pre-processing method, and RF models constructed using the 20 most competitive and discriminative spectral markers (selected by Venn) could achieve accurate identification of baijiu samples based on irradiation dose (0, 4, 6, and 8 kGy). After Pearson correlation analysis, the five significantly (P＜0.05) changed spectral markers (1596, 2025, 2309, 2329, and 2380 cm-1) were attributed to changes in the content of total acids, alcohols, and aromatic compounds. These findings demonstrate for the first time the potential of ATR-FTIR spectroscopy as a fast, low-cost, and non-destructive tool for the characterization and identification of irradiated baijiu samples. This approach may also offer a promising solution for labeling management of irradiated foods, vintage identification of baijius, and brand protection.

The effect of SARS-CoV-2 variants on the plasma oxylipins and PUFAs of COVID-19 patients

Oxylipins are important signalling compounds that are significantly involved in the regulation of the immune system and the resolution of inflammation. Lipid metabolism is strongly activated upon SARS-CoV-2 infection, however the modulating effects of oxylipins induced by different variants remain unexplored. Here, we compare the plasma profiles of thirty-seven oxylipins and four PUFAs in subjects infected with Wild-type, Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron (B.1.1.529) variants. The results suggest that oxidative stress and inflammation resulting from COVID-19 were highly dependent on the SARS-CoV-2 variant, and that the Wild-type elicited the strongest inflammatory storm. The Alpha and Delta variants induced a comparable lipid profile alteration upon infection, which differed significantly from Omicron. The latter variant increased the levels of pro-inflammatory mediators and decreased the levels of omega-3 PUFA in infected patients. We speculate that changes in therapeutics, vaccination, and prior infections may have a role in the alteration of the oxylipin profile besides viral mutations. The results shed new light on the evolution of the inflammatory response in COVID-19.

An early asymptomatic diagnosis method for cork spot disorder in 'Akizuki' pear (Pyrus pyrifolia Nakai) using micro near infrared spectroscopy

The early symptoms of cork spot disorder in 'Akizuki' pear (Pyrus pyrifolia Nakai) are challenging to distinguish from those in healthy fruits, hindering early identification in production. In this study, samples of cork-browned 'Akizuki' pears, asymptomatic fruits and healthy fruits were examined to determine the content of relevant mineral elements. A micro near-infrared spectrometer collected spectral information, and various pretreatment methods were applied to the near-infrared spectral data. Support vector machine (SVM) modelling using the original data achieved the highest overall recognition accuracy of 84.65% and an F1 value of 84.06%. For identifying fruits without cork spot disease, Autokeras modelled data processed with the SG method, achieving the best accuracy of 90%. These findings establish a reliable basis for the early identification and diagnosis of cork spot disorder in 'Akizuki' pear, enhancing pear production management.

Supervised classification combined with genetic algorithm variable selection for a fast identification of polymeric microdebris using infrared reflectance

Pollution caused by plastics and, in particular, microplastics has become a source of environmental concern for Society. Their ubiquity, with millions of tons of plastic debris spilled in both land and sea, requires efficient technological improvements in the ways residues are collected, handled, characterized and recycled. For reliable decision-making, dependable chemical information is essential to assess both the nature of the plastics found in the environment and their fate. In this work an efficient method to identify the polymeric composition of microplastic fragments is proposed. It combines infrared reflectance spectra and chemometric methods. A breakthrough result is that the models include polymers weathered under both dry (shoreline) and submerged (in sea water) conditions and, hence, they are very promising as a starting point for eventual practical applications. In addition, no spectral processing is required after the initial measurement. SYNOPSIS: This approach to identify microplastics in aquatic environments combines infrared measurements and multivariate data analysis to fight against (micro)plastic pollution.

Prediction of fatty acid composition in intact and minced fat of European autochthonous pigs breeds by near infrared spectroscopy

The fatty acids profile has been playing a decisive role in recent years, thanks to technological, sensory and health demands from producers and consumers. The application of NIRS technique on fat tissues, could lead to more efficient, practical, and economical in the quality control. The study aim was to assess the accuracy of Fourier Transformed Near Infrared Spectroscopy technique to determine fatty acids composition in fat of 12 European local pig breeds. A total of 439 spectra of backfat were collected both in intact and minced tissue and then were analyzed using gas chromatographic analysis. Predictive equations were developed using the 80% of samples for the calibration, followed by full cross validation, and the remaining 20% for the external validation test. NIRS analysis of minced samples allowed a better response for fatty acid families, n6 PUFA, it is promising both for n3 PUFA quantification and for the screening (high, low value) of the major fatty acids. Intact fat prediction, although with a lower predictive ability, seems suitable for PUFA and n6 PUFA while for other families allows only a discrimination between high and low values.

Pre-processing Applied to Instrumental Data in Analytical Chemistry: A Brief Review of the Methods and Examples

The field of analytical chemistry has been significantly advanced by the availability of state-of-the-art instrumentation, allowing for the development of novel applications in this field. However, in many cases, the direct interpretation of the recorded data is often not straightforward, hence some level of pre-processing is required (e.g., baseline correction, derivatives, normalization, smoothing). These techniques have become a critical first step for the successful analysis of the data recorded, and it is recommended to use them before the application of chemometrics (e.g., classification, calibration development). The aim of this paper is to provide with an overview of the most used pre-processing methods applied to instrumental analytical methods (e.g., spectroscopy, chromatography). Examples of their application in near infrared and UV-VIS spectroscopy as well as in gas chromatography will be also discussed. Overall, this paper provides with a comprehensive understanding of pre-processing techniques in analytical chemistry, highlighting their importance during the analysis and interpretation of data, as well as during the development of accurate and reliable chemometric models.

Infrared-Photoacoustic Spectroscopy and Multiproduct Multivariate Calibration to Estimate the Proportion of Coffee Defects in Roasted Samples

Infrared-photoacoustic spectroscopy (IR-PAS) and partial least squares (PLS) were tested as a rapid alternative to conventional methods to evaluate the proportion of coffee defects in roasted and ground coffees. Multiproduct multivariate calibration models were obtained from spectra of healthy beans of Coffea canephora and C. arabica (Arabica) and blends composed of defective and healthy beans of Arabica in different proportions. The blends, named selections, contained sour, black, broken, whole beans, skin, and coffee woods. Six models were built using roasted and ground coffee samples. The model was optimized through outlier evaluation, and the parameters of merit such as accuracy, sensitivity, limits of detection and quantification, the inverse of analytical sensitivity, linearity, and adjustment were computed. The models presented predictive capacity and high sensitivity in determining defects, all being predicted with suitable correlation coefficients (ranging from 0.7176 to 0.8080) and presenting adequate performance. The parameters of merit displayed promising results, and the prediction models developed for %defects can be safely used as an alternative to the reference method. Furthermore, the new method is fast, efficient, and suitable for in-line application in quality control industrial coffee processing.

Toward Achieving Rapid Estimation of Vitamin C in Citrus Peels by NIR Spectra Coupled with a Linear Algorithm

Citrus peels are rich in bioactive compounds such as vitamin C and extraction of vitamin C is a good strategy for citrus peel recycling. It is essential to evaluate the levels of vitamin C in citrus peels before reuse. In this study, a near-infrared (NIR)-based method was proposed to quantify the vitamin C content of citrus peels in a rapid way. The spectra of 249 citrus peels in the 912-1667 nm range were acquired, preprocessed, and then related to measured vitamin C values using the linear partial least squares (PLS) algorithm, indicating that normalization correction (NC) was more suitable for spectral preprocessing and NC-PLS model built with full NC spectra (375 wavelengths) showed a better performance in predicting vitamin C. To accelerate the predictive process, wavelength selection was conducted, and 15 optimal wavelengths were finally selected from NC spectra using the stepwise regression (SR) method, to predict vitamin C using the multiple linear regression (MLR) algorithm. The results showed that SR-NC-MLR model had the best predictive ability with correlation coefficients (r_P) of 0.949 and root mean square error (RMSE_P) of 14.814 mg/100 mg in prediction set, comparable to the NC-PLS model in predicting vitamin C. External validation was implemented using 40 independent citrus peels samples to validate the suitability of the SR-NC-MLR model, obtaining a good correlation (R² = 0.9558) between predicted and measured vitamin C contents. In conclusion, it was reasonable and feasible to achieve the rapid estimation of vitamin C in citrus peels using NIR spectra coupled with MLR algorithm.

Towards the non-destructive analysis of multilayered samples: A novel XRF-VNIR-SWIR hyperspectral imaging system combined with multiblock data processing

The new challenge in the investigation of cultural heritage is the possibility to obtain stratigraphical information about the distribution of the different organic and inorganic components without sampling. In this paper recently commercialized analytical set-up, which is able to co-register VNIR, SWIR, and XRF spectral data simultaneously, is exploited in combination with an innovative multivariate and multiblock high-throughput data processing for the analysis of multilayered paintings. The instrument allows to obtain elemental and molecular information from superficial to subsurface layers across the investigated area. The chemometric strategy proved to be highly efficient in data reduction and for the extraction and integration of the most useful information coming from the three different spectroscopies, also filling the gap between data acquisition and data understanding through the combination of principal component analysis (PCA), brushing, correlation diagrams and maps (within and between spectral blocks) on the low-level fused. In particular, correlation diagrams and maps provide useful information for the reconstruction of a stratigraphic structure without the need to take any sample, thanks to the effective account for inter-correlation among data (variables), which is able to effectively characterize the possible combinations of components located in the same depth level. The highly innovative technology and the data processing strategy are applied for the multi-level characterization of a complex painting reproduction as an illustrative pilot study.

How Chemometrics Can Fight Milk Adulteration

Adulteration and fraud are amongst the wrong practices followed nowadays due to the attitude of some people to gain more money or their tendency to mislead consumers. Obviously, the industry follows stringent controls and methodologies in order to protect consumers as well as the origin of the food products, and investment in these technologies is highly critical. In this context, chemometric techniques proved to be very efficient in detecting and even quantifying the number of substances used as adulterants. The extraction of relevant information from different kinds of data is a crucial feature to achieve this aim. However, these techniques are not always used properly. In fact, training is important along with investment in these technologies in order to cope effectively and not only reduce fraud but also advertise the geographical origin of the various food and drink products. The aim of this paper is to present an overview of the different chemometric techniques (from clustering to classification and regression applied to several analytical data) along with spectroscopy, chromatography, electrochemical sensors, and other on-site detection devices in the battle against milk adulteration. Moreover, the steps which should be followed to develop a chemometric model to face adulteration issues are carefully presented with the required critical discussion.

A Multi-Analytical Approach on Silver-Copper Coins of the Roman Empire to Elucidate the Economy of the 3rd Century A.D

In this study, 160 silver-copper alloy denarii and antoniniani from the 3rd century A.D. were studied to obtain their overall chemical composition. The approach used for their characterisation is based on a combination of physical, chemical, and chemometric techniques. The aim is to identify and quantify major and trace elements in Roman silver-copper coins in order to assess changes in composition and to confirm the devaluation of the currency. After a first cataloguing step, μ-EDXRF and SEM-EDX techniques were performed to identify the elements on the coins’ surface. A micro-destructive sampling method was employed on a representative sample of the coins to quantify the elements present in the bulk. The powder obtained from drilling 12 coins (keeping the two categories of coins separate) was dissolved in an acidic medium; heated and sonicated to facilitate dissolution; and then analysed by ICP-AES and ICP-MS. The two currencies had different average alloy percentages; in particular, the % difference of Ag was about 8%. The other elements were found in concentrations <1 wt%. Of these, the element highest in concentration were Pb and Sn, which is in agreement with the literature. The multivariate analysis performed on the data acquired revealed two groups of coins, corresponding to the two currencies.

Application on infrared spectroscopy for the analysis of total phenolic compounds in fruits

Recent studies have demonstrated the metabolic benefits of phenolic compounds on human health. However, traditional analytical methods used for quantification of total phenolic compounds are time-consuming, laborious, require a high volume of reagents, mostly toxic substances, and involve several steps that can result in systematic and instrumental errors. Spectroscopic techniques have been used as alternatives to these methods for the determination of bioactive compounds directly in the food matrix by minimal sample preparation, without using toxic reagents. Therefore, this overview presents the advantages of nondestructive methods focusing on infrared spectroscopy (IR), for the quantification of total phenolic compounds in fruits. In addition, the main difficulties in applying these spectroscopic techniques are presented, as well as a comparison between the quantification of total phenolic compounds by traditional and IR methods. This review concludes by focusing on model building, highlighting that IR data are mainly processed using the partial least-squares (PLS) regression method to predict total phenolic content. The development of portable and inexpensive IR instruments, combined with multivariate data processing, could give to the consumers a straightforward technology to evaluate the total phenolic content of fruits prior to purchase.

TXRF spectral information enhanced by multivariate analysis: A new strategy for food fingerprint

The increased costumers' request of safe and high-quality food products makes food traceability a priority for frauds identification and quality certification. Elemental profiling is one of the strategies used for food traceability, and TXRF spectroscopy is widely used in food analysis even if its potentialities have not been fully investigated. In this work, a new method for food traceability using directly TXRF spectra coupled with multivariate analyses, was tested. Twenty-four different beans' genotypes (Phaseolus vulgaris L.) grown onto two different sites have been studied. After the development of the method for beans' analysis, TXRF spectra were collected and processed with PCA combined with SNV and GLSW filter obtaining a perfect clustering of the seeds according to their geographical origin. Finally, using PLS-DA, beans were correctly classified demonstrating that TXRF spectra can be successfully used as fingerprint for food/seed traceability and that elemental quantification procedure is not necessary to this aim.

Theoretical Principles and Perspectives of Hyperspectral Imaging Applied to Sediment Core Analysis

Hyperspectral imaging is a recent technology that has been gaining popularity in the geosciences since the 1990s, both in remote sensing and in the field or laboratory. Indeed, it allows the rapid acquisition of a large amount of data that are spatialized on the studied object with a low-cost, compact, and automatable sensor. This practical article aims to present the current state of knowledge on the use of hyperspectral imaging for sediment core analysis (core logging). To use the full potential of this type of sensor, many points must be considered and will be discussed to obtain reliable and quality data to extract many environmental properties of sediment cores. Hyperspectral imaging is used in many fields (e.g., remote sensing, geosciences and artificial intelligence) and offers many possibilities. The applications of the literature will be reviewed under five themes: lake and water body trophic status, source-to-sink approaches, organic matter and mineralogy studies, and sedimentary deposit characterization. Afterward, discussions will be focused on a multisensor core logger, data management, integrated use of these data for the selection of sample areas, and other opportunities. Through this practical article, we emphasize that hyperspectral imaging applied to sediment cores is still an emerging tool and shows many possibilities for refining the understanding of environmental processes.

A Preliminary Color Study of Different Basil-Based Semi-Finished Products during Their Storage

Basil-based semi-finished products, which are mainly used as an intermediate to produce the typical pesto sauce, are prepared and exported all over the world. Color is a fundamental organoleptic requirement for the acceptability of these semi-finished products by the manufacturers of the pesto sauce. Some alternative formulations, which adjust the typical industrial recipe by both changing the preservative agent (ascorbic acid, citric acid, or a mixture of both) and introducing a preliminary thermic treatment (blast chilling), were evaluated. In this work, a fast and non-destructive spectrophotometric analysis, to monitor the color variations in these food products during their shelf-life, was proposed. The raw diffuse reflectance spectra (380-900 nm) obtained by a UV-visible spectrophotometer, endowed with an integrating sphere, together with the CIELab parameters (L*, a*, b*) automatically obtained from these, were considered, and elaborated using multivariate statistical analysis (principal component analysis). From this preliminary study, blast chilling, together with the use of ascorbic acid, proved to be the best solution to better preserve the color of these products during their shelf-life.

Multi-Elemental Analysis as a Tool to Ascertain the Safety and the Origin of Beehive Products: Development, Validation, and Application of an ICP-MS Method on Four Unifloral Honeys Produced in Sardinia, Italy

Despite unifloral honeys from Sardinia, Italy, being appreciated worldwide for their peculiar organoleptic features, their elemental signature has only partly been investigated. Hence, the principal aim of this study was to measure the concentration of trace and toxic elements (i.e., Ag, As, Ba, Be, Bi, Cd, Co, Cr, Cu, Fe, Hg, Li, Mn, Mo, Ni, Pb, Sb, Sn, Sr, Te, Tl, V, and Zn) in four unifloral honeys produced in Sardinia. For this purpose, an original ICP-MS method was developed, fully validated, and applied on unifloral honeys from asphodel, eucalyptus, strawberry tree, and thistle. Particular attention was paid to the method’s development: factorial design was applied for the optimization of the acid microwave digestion, whereas the instrumental parameters were tuned to minimize the polyatomic interferences. Most of the analytes’ concentration ranged between the relevant LoDs and few mg kg⁻¹, while toxic elements were present in negligible amounts. The elemental signatures of asphodel and thistle honeys were measured for the first time, whereas those of eucalyptus and strawberry tree honeys suggested a geographical differentiation if compared with the literature. Chemometric analysis allowed for the botanical discrimination of honeys through their elemental signature, whereas linear discriminant analysis provided an accuracy level of 87.1%.

MS-based targeted profiling of oxylipins in COVID-19: A new insight into inflammation regulation

The key role of inflammation in COVID-19 induced many authors to study the cytokine storm, whereas the role of other inflammatory mediators such as oxylipins is still poorly understood. IMPRECOVID was a monocentric retrospective observational pilot study with COVID-19 related pneumonia patients (n = 52) admitted to Pisa University Hospital between March and April 2020. Our MS-based analytical platform permitted the simultaneous determination of sixty plasma oxylipins in a single run at ppt levels for a comprehensive characterisation of the inflammatory cascade in COVID-19 patients. The datasets containing oxylipin and cytokine plasma levels were analysed by principal component analysis (PCA), computation of Fisher's canonical variable, and a multivariate receiver operating characteristic (ROC) curve. Differently from cytokines, the panel of oxylipins clearly differentiated samples collected in COVID-19 wards (n = 43) and Intensive Care Units (ICUs) (n = 27), as shown by the PCA and the multivariate ROC curve with a resulting AUC equal to 0.92. ICU patients showed lower (down to two orders of magnitude) plasma concentrations of anti-inflammatory and pro-resolving lipid mediators, suggesting an impaired inflammation response as part of a prolonged and unsolvable pro-inflammatory status. In conclusion, our targeted oxylipidomics platform helped shedding new light in this field. Targeting the lipid mediator class switching is extremely important for a timely picture of a patient's ability to respond to the viral attack. A prediction model exploiting selected lipid mediators as biomarkers seems to have good chances to classify patients at risk of severe COVID-19.

OxInflammation at High Altitudes: A Proof of Concept from the Himalayas

High-altitude locations are fascinating for investigating biological and physiological responses in humans. In this work, we studied the high-altitude response in the plasma and urine of six healthy adult trekkers, who participated in a trek in Nepal that covered 300 km in 19 days along a route in the Kanchenjunga Mountain and up to a maximum altitude of 5140 m. Post-trek results showed an unbalance in redox status, with an upregulation of ROS (+19%), NOx (+28%), neopterin (+50%), and pro-inflammatory prostanoids, such as PGE₂ (+120%) and 15-deoxy-delta12,14-PGJ₂ (+233%). The isoprostane 15-F_2t-IsoP was associated with low levels of TAC (−18%), amino-thiols, omega-3 PUFAs, and anti-inflammatory CYP450 EPA-derived mediators, such as DiHETEs. The deterioration of antioxidant systems paves the way to the overload of redox and inflammative markers, as triggered by the combined physical and hypoxic stressors. Our data underline the link between oxidative stress and inflammation, which is related to the concept of OxInflammation into the altitude hypoxia fashion.

Analysing the water spectral pattern by near-infrared spectroscopy and chemometrics as a dynamic multidimensional biomarker in preservation: rice germ storage monitoring

Water activity is an important phenomenon not yet explained in terms of water molecular structure. This paper aims to find the relationship between the water activity and water molecular structure of the rice germ, based on its spectral pattern which can be measured using non-destructive technology. Aquaphotomics near-infrared spectroscopy was used to study rice germ stored at different levels of water activity and atmosphere. The findings show that state of the rice germ is governed by the water activity upon storage, which is defined by the structure of water within germ matrix. The structure of water can be described solely by the absorbance spectral pattern at the following absorbance bands: proton hydrates, hydration shells and water vapor (1364, 1375 and 1382 nm), trapped water (1392 nm), free water (1410 nm), hydration water (1425 nm), adsorbed water (1455 nm), non-bonded hydroxyl (1436 nm) and bound water (1520 nm).

Toward the Development of Combined Artificial Sensing Systems for Food Quality Evaluation: A Review on the Application of Data Fusion of Electronic Noses, Electronic Tongues and Electronic Eyes

Devices known as electronic noses (ENs), electronic tongues (ETs), and electronic eyes (EEs) have been developed in recent years in the in situ study of real matrices with little or no manipulation of the sample at all. The final goal could be the evaluation of overall quality parameters such as sensory features, indicated by the "smell", "taste", and "color" of the sample under investigation or in the quantitative detection of analytes. The output of these sensing systems can be analyzed using multivariate data analysis strategies to relate specific patterns in the signals with the required information. In addition, using suitable data-fusion techniques, the combination of data collected from ETs, ENs, and EEs can provide more accurate information about the sample than any of the individual sensing devices. This review's purpose is to collect recent advances in the development of combined ET, EN, and EE systems for assessing food quality, paying particular attention to the different data-fusion strategies applied.

Chemometric strategies for near infrared hyperspectral imaging analysis: classification of cotton seed genotypes

Hyperspectral images have been increasingly employed in the agricultural sector for seed classification for different purposes. In the present paper we propose a new methodology based on HSI in the near infrared range (HSI-NIR) to distinguish conventional from transgenic cotton seeds. Three different chemometric approaches, one pixel-based and two object-based, using partial least squares discriminant analysis (PLS-DA) were built and their performances were compared considering the pros and cons of each approach. Specificity and sensitivity values ranged from 0.78-0.92 and 0.62-0.93, respectively, for the different approaches.

The Legacy of the Idrija Mine Twenty-Five Years after Closing: Is Mercury in the Water Column of the Gulf of Trieste Still an Environmental Issue?

Mercury (Hg) contamination in the Gulf of Trieste (northern Adriatic Sea) due to mining activity in Idrija (Slovenia) still represents an issue of environmental concern. The Isonzo/Soča River's freshwater inputs have been identified as the main source of Hg into the Gulf, especially following periods of medium-high discharge. This research aims to evaluate the occurrence and distribution of dissolved (DHg) and particulate (PHg) Hg along the water column in the northernmost sector of the Gulf, a shallow and sheltered embayment suitable for the accumulation of fine sediments. Sediment and water samples were collected under unperturbed and perturbed environmental conditions induced by natural and anthropogenic factors. Mercury in the sediments (0.77-6.39 µg g-1) and its relationship to grain size were found to be consistent with previous research focused on the entire Gulf, testifying to the common origin of the sediment. Results showed a notable variability of DHg ( Collapse

Key Words

• mercury
• sediments
• suspended particulate matter
• water column

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MESH Headings

• Environmental Monitoring
• Geologic Sediments
• Mercury/analysis
• Mining
• Rivers
• Water
• Water Pollutants, Chemical/analysis

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Grants Collapse

Affiliation(s)

Elena Pavoni Dipartimento di Matematica e Geoscienze, Università Degli Studi di Trieste, Via Weiss 2, 34128 Trieste, Italy; (E.P.); (E.P.); (G.F.)
Elisa Petranich Dipartimento di Matematica e Geoscienze, Università Degli Studi di Trieste, Via Weiss 2, 34128 Trieste, Italy; (E.P.); (E.P.); (G.F.)
Sergio Signore Autorità di Sistema Portuale del Mare Adriatico Orientale-Porto di Trieste, Via Karl Ludwig Von Bruck 3, 34144 Trieste, Italy;
Giorgio Fontolan Dipartimento di Matematica e Geoscienze, Università Degli Studi di Trieste, Via Weiss 2, 34128 Trieste, Italy; (E.P.); (E.P.); (G.F.)
Stefano Covelli Dipartimento di Matematica e Geoscienze, Università Degli Studi di Trieste, Via Weiss 2, 34128 Trieste, Italy; (E.P.); (E.P.); (G.F.)

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Method Superior to Traditional Spectral Identification: FT-NIR Two-Dimensional Correlation Spectroscopy Combined with Deep Learning to Identify the Shelf Life of Fresh Phlebopus portentosus

The taste of fresh mushrooms is always appealing. Phlebopus portentosus is the only porcini that can be cultivated artificially in the world, with a daily output of up to 2 tons and a large sales market. Fresh mushrooms are very susceptible to microbial attacks when stored at 0-2 °C for more than 5 days. Therefore, the freshness of P. portentosus must be evaluated during its refrigeration to ensure food safety. According to their freshness, the samples were divided into three categories, namely, category I (1-2 days, 0-48 h, recommended for consumption), category II (3-4 days, 48-96 h, recommended for consumption), and category III (5-6 days, 96-144 h, not recommended). In our study, a fast and reliable shelf life identification method was established through Fourier transform near-infrared (FT-NIR) spectroscopy combined with a machine learning method. Deep learning (DL) is a new focus in the field of food research, so we established a deep learning classification model, traditional support-vector machine (SVM), partial least-squares discriminant analysis (PLS-DA), and an extreme learning machine (ELM) model to identify the shelf life of P. portentosus. The results showed that FT-NIR two-dimensional correlation spectroscopy (2DCOS) combined with the deep learning model was more suitable for the identification of fresh mushroom shelf life and the model had the best robustness. In conclusion, FT-NIR combined with machine learning had the advantages of being nondestructive, fast, and highly accurate in identifying the shelf life of P. portentosus. This method may become a promising rapid analysis tool, which can quickly identify the shelf life of fresh edible mushrooms.

Development of a Near Infrared Spectroscopy method for the in-line quantitative bilastine drug determination during pharmaceutical powders blending

The Food and Drug Administration (FDA)'s guidelines and the Process Analytical Technology (PAT) approach conceptualize the idea of real time monitoring of a process, with the primary objective of improvement of quality and also of time and resources saving. New instruments are needed to perform an efficient PAT process control and Near Infrared Spectroscopy (NIRS), thanks to its rapid and drastic development of last years, could be a very good choice, in virtue of its high versatility, speed of analysis, non-destructiveness and absence of sample chemical treatment. This work was aimed to develop a NIR analytical method for bilastine assay in powder mixtures for direct compression. In particular, the use of NIR instrumentation should allow to control the bilastine concentration and the whole blending process, assuring the achievement of a homogeneous blend. The commercial tablet formulation of bilastine was particularly suitable for this purpose, due to its simple composition (four excipients) and direct compression manufacturing process. Calibration and validation set were prepared according to a Placket-Burman experimental design and acquired with a miniaturized NIR in-line instrument (MicroNIR by Viavi Solution Inc.). Chemometric was applied to optimize information extraction from spectra, by subjecting them to a Standard Normal Variate (SNV) and a Savitzky-Golay second derivative pre-treatment. This spectra pre-treatment, combined with the most suitable wavelength interval (resulted between 1087 and 1217 nm), enabled to obtain a Partial Least Square (PLS) model with a good predictive ability. The selected model, tried on laboratory and production batches, provided in both cases good assay predictions. Results were confirmed by traditional HPLC (High Performance Liquid Chromatography) API (Active Pharmaceutical Ingredient) content uniformity test on the final product.

Relating Near-Infrared Light Path-Length Modifications to the Water Content of Scattering Media in Near-Infrared Spectroscopy: Toward a New Bouguer-Beer-Lambert Law

In near-infrared spectroscopy (NIRS), the linear relationship between absorbance and an absorbing compound concentration has been strictly defined by the Bouguer-Beer-Lambert law only for the case of transmission measurements of nonscattering media. However, various quantitative calibrations have been successfully built both on reflectance measurements and for scattering media. Although the lack of linearity for scattering media has been observed experimentally, the sound multivariate statistics and signal processing involved in chemometrics have allowed us to overcome this problem in most cases. However, in the case of samples with varying water content, important modifications of scattering levels still make calibrations difficult to build due to nonlinearities. Moreover, even when calibration procedures are successfully developed, many preprocessing methods used do not guarantee correct spectroscopic assignments (in the sense of a pure chemical absorbance). In particular, this may prevent correct modeling and interpretation of the structure of water. In this study, dynamic near-infrared spectra acquired during a drying process allow the study of the physical effects of water content variations, with a focus on the first overtone OH absorbance region. A model sample consisting of aluminum pellets mixed with water allowed us to study this specifically, without any other absorbing interaction terms related to the dry mass-absorbing constituents. A new formulation of the Bouguer-Beer-Lambert law is proposed, by expressing path length as a power function of water content. Through this new formulation, it is shown that a better and simpler prediction model of water content may be developed, with more precise and accurate identification of water absorbance bands.

The Impact of Preprocessing Methods for a Successful Prostate Cell Lines Discrimination Using Partial Least Squares Regression and Discriminant Analysis Based on Fourier Transform Infrared Imaging

Fourier transform infrared spectroscopy (FT-IR) is widely used in the analysis of the chemical composition of biological materials and has the potential to reveal new aspects of the molecular basis of diseases, including different types of cancer. The potential of FT-IR in cancer research lies in its capability of monitoring the biochemical status of cells, which undergo malignant transformation and further examination of spectral features that differentiate normal and cancerous ones using proper mathematical approaches. Such examination can be performed with the use of chemometric tools, such as partial least squares discriminant analysis (PLS-DA) classification and partial least squares regression (PLSR), and proper application of preprocessing methods and their correct sequence is crucial for success. Here, we performed a comparison of several state-of-the-art methods commonly used in infrared biospectroscopy (denoising, baseline correction, and normalization) with the addition of methods not previously used in infrared biospectroscopy classification problems: Mie extinction extended multiplicative signal correction, Eiler’s smoothing, and probabilistic quotient normalization. We compared all of these approaches and their effect on the data structure, classification, and regression capability on experimental FT-IR spectra collected from five different prostate normal and cancerous cell lines. Additionally, we tested the influence of added spectral noise. Overall, we concluded that in the case of the data analyzed here, the biggest impact on data structure and performance of PLS-DA and PLSR was caused by the baseline correction; therefore, much attention should be given, especially to this step of data preprocessing.

Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy

Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory. Samples of billets were collected and four modes of scanning and sample preparation were evaluated: outer-surface (‘skin’) (SS), cross-sectional scanning (CSS), defibrated cane (DF), and raw juice (RJ) to analyze the parameters soluble solids content (Brix), saccharose (Pol), fibre, pol of cane and total recoverable sugars (TRS). Predictive models based on Partial Least Square Regression (PLSR) were built with the vis-NIR spectral measurements. There was no significant difference (p-value > 0.05) between the accuracy SS and CSS samples compared to DF and RJ samples for all prediction models. However, DF samples presented the best predictive performance values for the main sugarcane quality parameters, and required only minimal sample preparation. The results contribute to advancing the development of on-board quality monitoring in sugarcane, indicating better sampling strategies.

Unveiling non-linear water effects in near infrared spectroscopy: A study on organic wastes during drying using chemometrics

In the context of organic waste management, near infrared spectroscopy (NIRS) is being used to offer a fast, non-destructive, and cost-effective characterization system. However, cumbersome freeze-drying steps of the samples are required to avoid water's interference on near infrared spectra. In order to better understand these effects, spectral variations induced by dry matter content variations were obtained for a wide variety of organic substrates. This was made possible by the development of a customized near infrared acquisition system with dynamic highly-resolved simultaneous scanning of near infrared spectra and estimation of dry matter content during a drying process at ambient temperature. Using principal components analysis, the complex water effects on near infrared spectra are detailed. Water effects are shown to be a combination of both physical and chemical effects, and depend on both the characteristics of the samples (biochemical type and physical structure) and the moisture content level. This results in a non-linear relationship between the measured signal and the analytical characteristic of interest. A typology of substrates with respect to these water effects is provided and could further be efficiently used as a basis for the development of local quantitative calibration models and correction methods accounting for these water effects.